We propose to improve methods for locus-specific resequencing using variation detection arrays (VDAs), also known as resequencing microarrays. High throughput genotyping technology provides a means to determine the diversity of the genomes of humans and other model organisms, and thereby more efficiently resolve the functional causes of disease. Recently microarray probe densities have increased significantly and costs have been reduced such that it has become economical to use VDAs for large scale SNP discovery. Recent improvements in gene mapping technologies have identified many disease-linked loci in humans, but efficient technologies for resequencing of these loci for large numbers of samples are needed because of low penetrance and complex disease etiologies in human populations. VDAs provide one such solution. We propose to improve the design of VDAs and the methods for automatic genotype base calling of VDA data. Our approach is based on modeling the physical DNA-DNA hybridization on the surface of the microarray and optimizing chip design for better performance under uniform hybridization conditions. Specifically, we propose to 1. Increase the call rate and accuracy of base calling on resequencing microarray data by using physical models that predict'hybridization results based on probe-target sequences, 2. Modify the probe sequence design of resequencing microarrays to achieve greater sensitivity and single nucleotide discrimination, 3. Assess the effects of our novel design strategy by hybridizing a diverse set of samples to a test microarray interrogating select ENCODE regions of the human genome, and 4. Demonstrate the advantages of our improved methods on a preliminary case study of a low penetrance modifier loci implicated in breast cancer susceptibility. This work aims to improve the effectiveness of resequencing technology, thereby improving the efficiency of variation detection, and ultimately leading to a better understanding of the molecular basis of complex human disease.